Exhaust gas purifying apparatus for internal combustion engine

ABSTRACT

An exhaust gas purifying apparatus for an internal combustion engine having a lean NOx catalyst in an exhaust system is provided. The lean NOx catalyst traps NOx in exhaust gases when the exhaust gases are in an oxidizing state, and discharges the trapped NOx when the exhaust gases are in an reducing state. In this apparatus, an estimated trapped NOx amount which is an estimated value of an amount of NOx trapped in the lean NOx catalyst, is calculated using a neural network. Engine operating parameters indicative of an operating condition of the engine are input, and the neural network outputs at least one control parameter which is relevant to the lean NOx catalyst. A reducing process of the NOx trapped in the lean NOx catalyst is performed according to the estimated trapped NOx amount.

TECHNICAL FIELD

The present invention relates to an exhaust gas purifying apparatus foran internal combustion engine, and particularly to an exhaust gaspurifying apparatus for the internal combustion engine having a lean NOxcatalyst in the exhaust system for removing NOx.

BACKGROUND ART

The patent document 1 shown below discloses an exhaust gas purifyingapparatus for an internal combustion engine having a lean NOx catalystin the exhaust system. The lean NOx catalyst traps NOx and reduces thetrapped NOx. Since NOx is trapped in the lean NOx catalyst during a leanoperation wherein the air-fuel ratio is set to a comparatively largevalue, it is necessary to timely supply reducing components (HC, CO) tothe lean NOx catalyst so as to reduce the trapped NOx. In the apparatusshown in the patent document 1, an amount of NOx trapped in the lean NOxcatalyst is estimated according to an engine rotational speed and ademand torque of the engine. The NOx amount in the feed gas (gasdischarged from the combustion chamber of the engine) changes dependingon an exhaust gas recirculation amount (an amount of exhaust gasesrecirculated to the intake system). Therefore, in the apparatus of thepatent document 1, the amount of trapped NOx is corrected according tothe exhaust gas recirculation amount.

The patent document 2 shown below discloses a NOx removing apparatuswhich removes NOx by the selective catalytic reduction (SCR) using urea.In this apparatus, a NOx emission amount is calculated using a neuralnetwork, and a supply amount of urea liquid is adjusted according to thecalculated NOx emission amount. An intake pressure, an intake pipetemperature, a fuel consumption rate, etc. are used as input parametersof the neural network and the output parameter of the neural network isan amount of NOx emitted from the engine.

-   [Patent Document 1] Japanese Patent Laid Open No. 2006-214322-   [Patent Document 2] Japanese Patent Laid Open No. 2003-328732

DISCLOSURE OF THE INVENTION Problems to be Solved by the Invention

In the apparatus shown in the patent document 1, a deviation between adetected intake air flow rate and a target intake air flow rate is usedas an exhaust gas recirculation amount parameter indicative of an actualexhaust gas recirculation amount, and a correction of the trapped NOxamount is performed according to the exhaust gas recirculation amountparameter. However, the exhaust gas recirculation amount is influencedby various factors, such as the boost pressure, the fuel injectionamount, and the intake pressure. Therefore, it is difficult to estimatethe actual exhaust gas recirculation amount using the maps or the tableswhich are previously set to calculate the exhaust gas recirculationamount, or using the physical model, even if the engine is operating ina steady operating condition. In a transient operating condition of theengine, the difficulty further increases. Therefore, the exhaust gasrecirculation amount parameter shown in the patent document 1 does notaccurately indicate the actual exhaust gas recirculation amount, and thecorrection cannot be performed accurately.

Particularly when controlling a diesel engine, a closed-loop control isperformed wherein the exhaust gas recirculation amount is made tocoincide with a target value based on the air-fuel ratio detected by anair-fuel ratio sensor, a detected value of the intake air flow rate byan air flow meter and the like, and/or another closed-loop control isperformed wherein a vane opening of a turbocharger is adjusted so thatthe boost pressure coincides with a target pressure. Therefore, thecontrol systems performing the above closed-loop controls intricatelycooperate with each other, to accordingly compensate the characteristicvariation or aging changes in the exhaust gas recirculation amount.Therefore, an accurate trapped NOx amount cannot be obtained by thesimple method as shown the patent document 1, and the timing ofperforming a process for reducing NOx may be inappropriate, which mayincrease the NOx emission amount.

Further, in the apparatus of the patent document 2, the amount of NOxemitted from the engine is calculated using the neural network. However,the apparatus of the patent document 2 is not configured assuming thatthe lean NOx catalyst is used as the NOx removing device. Accordingly,the neural network is not applied for calculating an amount of NOxtrapped in the lean NOx catalyst. When using the lean NOx catalyst, thefunction as the NOx removing device is maintained by appropriatelyreducing the trapped NOx. However, in the apparatus of the patentdocument 1, NOx is reduced with ammonia generated from urea, so that NOxtrapping and reduction of the trapped NOx are not considered at all.

The present invention was made contemplating the above-described pointsand an object of the present invention is to provide an exhaust gaspurifying apparatus for an internal combustion engine, which accuratelyestimates an amount of NOx trapped in a lean NOx catalyst as a NOxremoving device, and appropriately performs the NOx reducing process.

Means for Solving the Problems

To attain the above object, the present invention provides an exhaustgas purifying apparatus for an internal combustion engine having a leanNOx catalyst in an exhaust system. The lean NOx catalyst traps NOx inexhaust gases when the exhaust gases are in an oxidizing state, anddischarges the trapped NOx when the exhaust gases are in an reducingstate. The exhaust gas purifying apparatus includes trapped NOx amountestimating means and reducing process means. The trapped NOx amountestimating means calculates an estimated trapped NOx amount (MNOx) whichis an estimated value of an amount of NOx trapped in the lean NOxcatalyst using a neural network. Engine operating parameters (Lcmd, PB,TI, PE, Tout, Rm1, GA, NE) indicative of an operating condition of theengine are input to the neural network and the neural network outputs atleast one control parameter (NOxhat, Redhat) which is relevant to thelean NOx catalyst. The reducing process means performs a reducingprocess of the NOx trapped in the lean NOx catalyst, according to theestimated trapped NOx amount.

With this configuration, the estimated trapped NOx amount is calculatedusing the neural network which outputs at least one control parameterrelevant to the lean NOx catalyst, wherein the engine operatingparameter are input to the neural network. Further, the reducing processof the NOx trapped in the lean NOx catalyst is performed according tothe estimated trapped NOx amount. The neural network is able to storenonlinear relationships among the input parameters. Accordingly, anaccurate value of the control parameter(s) can be obtained not only in asteady operating condition but also in a transient operating conditionof the engine, and hence the estimated trapped NOx amount can becalculated accurately. Consequently, the NOx reducing process isperformed at an appropriate timing to thereby maintain the NOx emissionamount at a low level.

Preferably, the neural network is a neural network to which aself-organizing map algorithm is applied.

With this configuration, the self-organizing map algorithm is applied tothe neural network. In the self-organizing map, a combination of theinput parameters and a pattern of appearance frequency of the inputparameters are stored as a distribution of neurons. Therefore, by usingthe data corresponding to the appearance frequency in the actual engineoperation as the training data, distribution intervals among neuronsbecomes comparatively narrow in the region corresponding to theoperating condition of high appearance frequency, which enablesestimation with high accuracy.

Preferably, the at least one control parameter relevant to the lean NOxcatalyst is an estimated value of an amount of NOx discharged from theengine or an estimated value of an amount of reducing componentsdischarged from the engine.

With this configuration, the estimated value of an amount of NOxdischarged from the engine or the estimated value of an amount ofreducing components discharged from the engine is output from the neuralnetwork, and the estimated trapped NOx amount is calculated using theoutput parameter of the neural network. The trapped NOx amount of thelean NOX catalyst depends on an amount of NOx flowing into the lean NOxcatalyst and an amount of NOx reduced with the in-flowing reducingcomponents. Accordingly, by calculating the estimated value of thein-flowing NOx amount and the estimated value of the in-flowing reducingcomponent amount using the nueral networks, estimation accuracy of theestimated trapped NOx amount is improved.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a configuration of an internal combustion engine and acontrol system therefor according to a first embodiment of the presentinvention.

FIG. 2 shows a self-organizing map.

FIG. 3 shows changes in an estimated exhaust gas recirculation amount(EGRhat) and an actual exhaust gas recirculation amount (EGRact) withchanges in engine operating parameters.

FIG. 4 is a flowchart of a fuel injection control process.

FIG. 5 shows maps referred to in the process of FIG. 4.

FIG. 6 shows maps referred to in the process of FIG. 4.

FIG. 7 illustrates a pilot injection and divided main injections offuel.

FIG. 8 is a flowchart of a process for performing a state determinationof an exhaust gas recirculation amount.

FIG. 9 illustrates relationships between the estimated exhaust gasrecirculation amount (EGRhat) and the actual exhaust gas recirculationamount (EGRact).

FIG. 10 is a flowchart of a process for performing a state determinationof the lean NOx catalyst.

FIG. 11 shows a map referred to in the process of FIG. 10.

FIG. 12 shows time charts for illustrating examples of the controloperation in the first embodiment.

FIG. 13 shows time charts for illustrating examples of the controloperation in the first embodiment.

FIG. 14 shows time charts for illustrating examples of the controloperation in the first embodiment.

FIG. 15 shows a configuration of an internal combustion engine and acontrol system therefor according to a second embodiment of the presentinvention.

FIG. 16 is a flowchart of a fuel injection control process according tothe second embodiment.

FIG. 17 is a flowchart of a fuel injection control process according tothe second embodiment.

FIG. 18 shows maps referred to in the process of FIG. 16.

FIG. 19 is a flowchart of a process for performing a state determinationof an exhaust gas recirculation control and a boost pressure control.

FIG. 20 is a flowchart of a process for performing a state determinationof the lean NOx catalyst.

FIG. 21 shows time charts for illustrating examples of the controloperation in the second embodiment.

FIG. 22 shows time charts for illustrating examples of the controloperation in the second embodiment.

FIG. 23 shows time charts for illustrating examples of the controloperation in the second embodiment.

FIG. 24 illustrates a perception for calculating the estimated exhaustgas recirculation amount (EGRhat).

FIG. 25 illustrates a perceptron for calculating an estimated NOxemission amount (NOxhat).

DESCRIPTION OF REFERENCE NUMERALS

-   -   1 Internal combustion engine    -   20 Electronic control unit (trapped NOx amount estimating means,        reducing process means)    -   31 Lean NOx catalyst

BEST MODE FOR CARRYING OUT THE INVENTION

Preferred embodiments of the present invention will now be describedwith reference to the drawings.

First Embodiment

FIG. 1 is a schematic diagram showing a configuration of an internalcombustion engine and a control system therefor according to a firstembodiment of the present invention. The internal combustion engine 1(hereinafter referred to as “engine”) is a diesel engine wherein fuel isinjected directly into the cylinders. Each cylinder is provided with afuel injection valve 9 that is electrically connected to an electroniccontrol unit 20 (hereinafter referred to as “ECU”). The ECU 20 controlsa valve opening timing and a valve opening period of each fuel injectionvalve 9.

The engine 1 has an intake pipe 2, an exhaust pipe 4, and a turbocharger8. The turbocharger 8 includes a turbine 11 and a compressor 16. Theturbine 11 has a turbine wheel 10 rotationally driven by the kineticenergy of exhaust gases. The compressor 16 has a compressor wheel 15connected to the turbine wheel 10 via a shaft 14. The compressor wheel15 pressurizes (compresses) the intake air of the engine 1.

The turbine 11 has a plurality of movable vanes 12 (only two areillustrated) and an actuator (not shown) for actuating the movable vanes12 to open and close. The plurality of movable vanes 12 are actuated toopen and close for changing a flow rate of exhaust gases that areinjected to the turbine wheel 10. The turbine 11 is configured so thatthe flow rate of exhaust gases injected to the turbine wheel 10 ischanged by changing an opening of the movable vane 12 (hereinafterreferred to as “vane opening”) θ vgt, to change the rotational speed ofthe turbine wheel 10. The actuator which actuates the movable vanes 12is connected to the ECU 20, and the vane opening θ vgt is controlled bythe ECU 20. Specifically, the ECU 20 supplies a control signal of avariable duty ratio to the actuator and controls the vane opening θ vgtby the control signal. The configuration of the turbocharger havingmovable vanes is widely known, for example, disclosed in Japanese PatentLaid-open No. H01-208501.

The intake pipe 2 is provided with an intercooler 18 downstream of thecompressor 16, and a throttle valve 3 downstream of the intercooler 18.The throttle valve 3 is configured to be actuated to open and close byan actuator 19, and the actuator 19 is connected to the ECU 20. The ECU20 performs an opening control of the throttle valve 3 through theactuator 19.

An exhaust gas recirculation passage 5 for recirculating exhaust gasesto the intake pipe 2 is provided between the exhaust pipe 4 and theintake pipe 2. The exhaust gas recirculation passage 5 is provided withan exhaust gas recirculation control valve 6 (hereinafter referred to as“EGR valve 6”) that controls the amount of exhaust gases (EGR amount)that are recirculated. The EGR valve 6 is an electromagnetic valvehaving a solenoid. A valve opening of the EGR valve 6 is controlled bythe ECU 20. The EGR valve 6 is provided with a lift sensor 7 fordetecting a valve opening (a valve lift amount) LACT, and the detectionsignal is supplied to the ECU 20. The exhaust gas recirculation passage5 and the EGR valve 6 constitute an exhaust gas recirculation device.The exhaust gas recirculation passage 5 is provided with an exhaust gasrecirculation amount sensor 26 for detecting an exhaust gasrecirculation amount EGRact, and the detection signal is supplied to theECU 20. The exhaust gas recirculation amount EGRact is actually detectedas a flow rate (mass/time).

An intake air flow rate sensor 21, a boost pressure sensor 22, an intakeair temperature sensor 23, and an intake pressure sensor 24 are disposedin the intake pipe 2. The intake air flow rate sensor 21 detects anintake air flow rate GA. The boost pressure sensor 22 detects an intakepressure (boost pressure) PB at a portion of the intake pipe 2downstream of the compressor 16. The intake air temperature sensor 23detects an intake air temperature TI. The intake pressure sensor 24detects an intake pressure PI. Further, an exhaust pressure sensor 25 isdisposed in the exhaust pipe 4. The exhaust pressure sensor 25 detectsan exhaust pressure PE at a portion of the exhaust pipe 4 upstream ofthe turbine 11. These sensors 21 to 25 are connected to the ECU 20, andthe detection signals from the sensors 21 to 25 are supplied to the ECU20.

A lean NOx catalyst 31 and a particulate filter 32 are disposeddownstream of the turbine 11 in the exhaust pipe 4. The lean NOxcatalyst 31 is a NOx removing device for removing NOx contained in theexhaust gases. The particulate filter 32 traps particulate matter (whichmainly consists of soot) contained in the exhaust gases. The lean NOxcatalyst 31 is configured so that NOx is trapped in a state where anoxygen concentration in the exhaust gases is comparatively high, i.e., aconcentration of reducing components (HC, CO) is comparatively low, andthe trapped NOx is reduced by the reducing components and discharged ina state where the reducing components concentration in the exhaust gasesis comparatively high.

An accelerator sensor 27, an engine rotational speed sensor 28, and anatmospheric pressure sensor 29 are connected to the ECU 20. Theaccelerator sensor 27 detects an operation amount AP of an acceleratorpedal (not shown) of the vehicle driven by the engine 1 (hereinafterpreferred to as “accelerator pedal operation amount AP”). The enginerotational speed sensor 28 detects an engine rotational speed NE. Theatmospheric pressure sensor 29 detects an atmospheric pressure PA. Thedetection signals of these sensors are supplied to the ECU 20. Theengine rotational speed sensor 28 supplies a crank angle pulse and a TDCpulse to the ECU 20. The crank angle pulse is generated at everypredetermined crank angle (e.g., 6 degrees). The TDC pulse is generatedin synchronism with a timing when a piston in each cylinder of theengine 1 is located at the top dead center.

The ECU 20 includes an input circuit, a central processing unit(hereinafter referred to as “CPU”), a memory circuit, and an outputcircuit. The input circuit performs various functions, including shapingthe waveforms of input signals from various sensors, correcting thevoltage levels of the input signals to a predetermined level, andconverting analog signal values into digital values. The memory circuitpreliminarily stores various operating programs to be executed by theCPU and stores the results of computations or the like by the CPU. Theoutput circuit supplies control signals to the actuator for actuatingthe movable vanes 12 of the turbine 11, the fuel injection valves 9, theEGR valve 6, the actuator 19 for actuating the throttle valve 3, and thelike.

The ECU 20 performs a fuel injection control with the fuel injectionvalve 9, an exhaust gas recirculation control with the EGR valve 6, aboost pressure control with the movable vanes 12, and the like accordingto an engine operating condition (which is indicated mainly by theengine rotational speed NE and an engine load target value Pmecmd). Theengine load target value Pmecmd is calculated according to theaccelerator pedal operation amount AP, and is set so as to increase asthe accelerator pedal operation amount AP increases.

In this embodiment, the fuel injection by the fuel injection valve 9 isperformed with a main injection MI and a pilot injection PI precedingthe main injection MI. Further, in a predetermined operating conditionof the engine 1 (specifically, a condition where the engine rotationalspeed NE is lower than a predetermined high speed NEH), the maininjection MI is performed by two divided injections, i.e., a first maininjection MI1 and a second main injection MI2. A dividing ratio Rm1 ofthe fuel injection amount is defined as “first main injectionamount/(first main injection amount+second main injection amount)”, andis basically set according to the engine rotational speed NE and theengine load target value Pmecmd.

Further, the ECU 20 calculates an estimated value of an exhaust gasrecirculation amount (hereinafter referred to as “estimated exhaust gasrecirculation amount”) EGRhat by using a neural network to which aself-organizing map algorithm is applied. This neural network ishereinafter referred to simply as “self-organizing map”. The ECU 20corrects the dividing ratio Rm1 using the estimated exhaust gasrecirculation amount EGRhat and the actual exhaust gas recirculationamount EGRact detected by the exhaust gas recirculation amount sensor26. By performing this correction, the influence of characteristicvariation or aging changes in the engine 1 and its peripheral devicesmounted on the engine 1 (e.g, the exhaust gas recirculation device, theintake air flow rate sensor) are suppressed, thereby improving therobustness of the NOx emission amount.

The self-organizing map will be hereinafter described in detail.

An input data vector xj which consists of “N” elements is defined by thefollowing equation (1), and a weighting vector wi of each neuron whichconstitutes the self-organizing map is defined by the following equation(2). A number of neurons is expressed by “M”. That is, a parameter “i”takes values from “1” to “M”. An initial value of the weighting vectorwi is given using a random number.

xj=(xj1, xj2, . . . , xjN)  (1)

wi=(wi1, wi2, . . . , wiN)  (2)

With respect to each of “M” neurons, an Euclid distance DWX (=|wi−xj|)between the input data vector xj and the weighting vector wi of thecorresponding neuron is calculated. A neuron whose distance DWX takes aminimum value is defined as the winner neuron. The Euclid distance DWXis calculated by the following equation (3).

$\begin{matrix}\left\lbrack {{Eq}.\mspace{14mu} 1} \right\rbrack & \; \\{{DWX} = \sqrt{\sum\limits_{k = 1}^{N}\; \left( {{wik} - {xjk}} \right)^{2}}} & (3)\end{matrix}$

Next, the weighting vectors wi of the winner neuron and neuronscontained in a neuron set Nc in the vicinity of the winner neuron areupdated by the following equation (4). In the equation (4), “α(t)” is atraining coefficient, and “t” is a number of times of training(hereinafter referred to simply as “training number”). The trainingcoefficient α(t) is, for example, set to “0.8” as an initial value, andset so as to decrease as the training number “t” increases.

wi(t+1)=wi(t)+α(t)(xj−wi(t))  (4)

The weighting vectors wi of the neurons which are not contained in theneuron set Nc maintain a preceding value as shown by the followingequation (5).

wi(t+1)=wi(t)  (5)

It is to be noted that the neuron set Nc is also a function of thetraining number “t”, and is set so that a range of the vicinity of thewinner neuron becomes narrow as the training number “t” increases. Theweighting vectors of the winner neuron and the neurons in the vicinityof the winner neuron are modified by updating with the equation (4) toapproach the input data vector.

If the calculation according to the training rule described above isperformed with respect to many input data vectors, the distribution of“M” neurons results in reflecting the distribution of the input datavectors. For example, when the input data vectors are simplified totwo-dimensional vectors and the distribution of the input data vectorsis expressed on a two-dimensional plane, the neurons distributeuniformly over the plane if the input data vectors distribute uniformlyover the plane. If nonuniformity is in the distribution of the inputdata vectors (if changes in the distribution density exist), thedistribution of the neurons finally becomes a distribution havingsimilar nonuniformity.

The self-organizing map obtained as described above may be furthermodified by applying the learning vector quantization (LVQ) algorithm,thereby obtaining a more suitable distribution of the neurons.

FIG. 2 shows a self-organizing map for calculating the estimated exhaustgas recirculation amount EGRhat in this embodiment, as a two-dimensionalmap. This two-dimensional map is defined by a lift amount command valueLcmd of the EGR valve 6 and the boost pressure PB which are two inputparameters as the most dominant factors. An input data vector xEGR isdefined by the following equation (10). That is, the input parametersare the lift amount command value Lcmd, the boost pressure PB, theintake air temperature TI, the exhaust pressure PE, a fuel injectionamount Tout, the intake air flow rate GA, and the engine rotationalspeed NE. The fuel injection amount Tout is a total fuel injectionamount in one combustion cycle including a pilot injection amount and amain injection amount described later.

xEGR=(Lcmd,PB,TI,PE,Tout,GA,NE)  (10)

The map shown in FIG. 2 is divided into a plurality of regions RNRi (i=1to M, M=36). Each region includes one neuron NRi (plotted with “*”). Bypreviously performing the training (learning) with a lot of input datavectors xEGR, the position (weighting vector wi) of each neuron NRi isdetermined. Each region RNRi is defined by drawing borderlines inconsideration of the positional relationships with the adjacent neurons.By making the distribution of the input data vector xEGR applied upontraining coincide with an actual distribution appearing in the actualengine operation, the distribution density of the neuron NRi becomescomparatively high in the regions corresponding to an engine operatingcondition with high appearance frequency during the actual engineoperation. With this feature of the self-organizing map, accuracy of theestimated exhaust gas recirculation amount in the operating conditionswith high appearance frequency can be improved. The map shown in FIG. 2is obtained by performing the training corresponding to the referenceengine (which is new and has an average operating characteristic). InFIG. 2, the input data applied to the training are plotted with blackdots.

When training the self-organizing map, a weighting coefficient vector Ci(i=1 to M) expressed by the following equation (11) is calculated andstored using the input data vector xEGR and the actual exhaust gasrecirculation amount EGRact corresponding to the input data vector xEGR.The weighting coefficient vector Ci is calculated and storedcorresponding to each neuron NRi.

Ci=(C0i,C1i,C2i,C3i,C4i,C5i,C6i,C7i)  (11)

In the actual control operation, the region RNRi which includes thepresent operating point on the map is first selected. The operatingpoint is defined by the lift amount command value Lcmd and the boostpressure PB which are elements of the input data vector xEGR. Next, theweighting coefficient vector Ci, which corresponds to the neuron NRirepresenting the region RNRi, and the input data vector xEGR are appliedto the following equation (12), to calculate the estimated exhaust gasrecirculation amount EGRhat.

$\begin{matrix}{{EGRhat} = {{C\; 1i \times {Lcmd}} + {C\; 2i \times {PB}} + {C\; 3i \times {TI}} + {C\; 4i \times {PE}} + {C\; 5i \times {Tout}} + {C\; 6i \times {GA}} + {C\; 7i \times {NE}} + {C\; 0i}}} & (12)\end{matrix}$

FIG. 3( a) is a time chart showing changes in the estimated exhaust gasrecirculation amount EGRhat and the actual exhaust gas recirculationamount EGRact. FIG. 3( b) is a time chart showing changes in thecorresponding main input data. From these figures, it is confirmed thataccurate calculated values of the estimated exhaust gas recirculationamount EGRhat are obtained not only in the steady state where the engineoperating condition is comparatively stable but also in the transientstate.

Next, the fuel injection control using the estimated exhaust gasrecirculation amount EGRhat is described below. FIG. 4 is a flowchart ofthe fuel injection control process. This process is executed by the CPUin the ECU 20 in synchronism with the TDC pulse.

In step S41, a Lcmd map shown in FIG. 5( a) is retrieved according tothe engine rotational speed NE and the engine load target value Pmecmd,to calculate the EGR valve lift amount command value Lcmd.

Pmecmd1, Pmecmd2, and Pmecmd3 shown in FIG. 5( a) are predeterminedengine load target values, and the relationship of“Pmecmd1<Pmecmd2<Pmecmd3” is satisfied. Therefore, when the engine loadtarget value Pmecmd increases from a value corresponding to a low-loadcondition, the EGR valve lift amount command value Lcmd increases untilthe predetermined engine load target value Pmecmd2. When the engine loadtarget value Pmecmd further increases, the EGR valve lift amount commandvalue Lcmd decreases. That is, according to the Lcmd map shown in FIG.5( a), the EGR valve lift amount command value Lcmd is set to acomparatively great value in order to reduce the NOx emission amount inthe ordinarily-used operating region of the engine (medium-speed andmedium-load engine operating region). Further, the EGR valve lift amountcommand value Lcmd is set to a comparatively small value in order toensure a sufficient output torque of the engine in a high-load engineoperating region. Further, the EGR valve lift amount command value Lcmdis set to a comparatively small value in order to ensure stablecombustion in a low-speed engine operating region.

In step S42, a PBcmd map shown in FIG. 5( b) is retrieved according tothe engine rotational speed NE and the engine load target value Pmecmd,to calculate a target boost pressure PBcmd. The PBcmd map is set in thelow-speed engine operating region so that the target boost pressurePBcmd increases as the engine rotational speed NE increases, and thetarget boost pressure PBcmd increases as the engine load target valuePmecmd increases. In the medium-speed and medium-load engine operatingregion, the target boost pressure PBcmd is set comparatively high inorder to ensure the fresh air amount since the EGR amount is increased.The vane opening θ vgt of the turbine is controlled according to thetarget boost pressure PBcmd.

In step S43, a Tout map shown in FIG. 5( c) is retrieved according tothe engine rotational speed NE and the engine load target value Pmecmd,to calculate the fuel injection amount Tout. The Tout map is set so thatthe fuel injection amount Tout increases as the engine rotational speedNE increases and/or the engine load target value Pmecmd increases. TheTout map is set so that an actual air-fuel ratio in the combustionchamber coincides with a target air-fuel ratio when the actual boostpressure PB coincides with the target boost pressure PBcmd; the actualexhaust gas recirculation amount EGRact coincides with a desired value;and an actual fuel injection amount coincides with the fuel injectionamount Tout.

In step S44, a state determination process of the exhaust gasrecirculation amount (EGR amount) shown in FIG. 8 is executed, tocalculate the estimated exhaust gas recirculation amount EGRhat, andcorrelation parameters a(k) and b(k) which indicate a relationshipbetween the estimated exhaust gas recirculation amount EGRhat and thedetected actual exhaust gas recirculation amount EGRact. “k” indicates acontrol time digitized with the execution period of this process.

When the correlation parameter a(k) calculated in step S44 takes a valuein the vicinity of “1.0”, this indicates that the actual exhaust gasrecirculation amount EGRact substantially coincides with the estimatedexhaust gas recirculation amount EGRhat approximating an actual exhaustgas recirculation amount of the reference engine. Further, when thecorrelation parameter a(k) is less than “1.0”, this indicates that theactual exhaust gas recirculation amount EGRact takes a smaller valuecompared with the estimated exhaust gas recirculation amount EGRhat,i.e., the actual exhaust gas recirculation amount EGRact is less than adesired value due to the characteristic variation or aging of theengine. Conversely, when the correlation parameter a(k) is greater than“1.0”, this indicates that the actual exhaust gas recirculation amountEGRact takes a greater value compared with the estimated exhaust gasrecirculation amount EGRhat, i.e., the actual exhaust gas recirculationamount EGRact is greater than the desired value due to thecharacteristic variation of the engine.

In step S45, the state determination process of the lean NOx catalystshown in FIG. 10 is performed to calculate a trapped NOx amount MNOxwhich is an amount of NOx trapped in the lean NOx catalyst 31 as well asto set a NOx reduction mode flag FRedMod. The NOx reduction mode flagFRedMod is set to “1” when performing a NOx reduction process in orderto reduce NOx trapped in the lean NOx catalyst 31. In the NOx reductionprocess, the post injection (fuel injection in the explosion strokeafter performing the main injection or in the exhaust stroke) isperformed for supplying reducing components to the exhaust system.

In step S46, a Toutp map shown in FIG. 6( a) is retrieved according tothe engine rotational speed NE and the engine load target value Pmecmd,to calculate a pilot injection fuel amount Toutp which is a fuelinjection amount of the pilot injection PI. The Toutp map is set so thatthe pilot injection amount Toutp increases in order to reduce thecombustion noise in the low-speed and low-load engine operating regionwherein an ignition delay of the injected fuel becomes comparativelygreat, and in the medium-speed and medium-load engine operating regionwherein the exhaust gas recirculation amount is increased.

In step S47, a Rm1 map shown in FIG. 6( b) is retrieved according to theengine rotational speed NE and the engine load target value Pmecmd, tocalculate a dividing ratio Rm1. The dividing ratio Rm1 is defined by thefollowing equation (13). In the equation (13), “Toutm1” is a first maininjection amount, and “Toutm2” is a second main injection amount.

Rm1=Toutm1/(Toutm1+Toutm2)  (13)

According to the Rm1 map, the dividing ratio Rm1 is set to “1.0” in thehigh-speed engine operating region, since the divided main injectionscannot be performed due the response delay of the fuel injection valve9. In the low-load and the medium-load engine operating regions, thedivided main injections are performed in order to reduce the NOxemission amount, and the Rm1 map is set so that the dividing ratio Rm1mostly decreases in the medium-speed and medium-load engine operatingregion. Further, in the high-load engine operating region, the dividedmain injections are performed in order to reduce the maximum cylinderpressure, thereby reducing the combustion noise.

The typical relationship of the pilot injection amount Toutp, the firstmain injection amount Toutm1, and the second main injection amountToutm2 is shown in FIG. 7. The horizontal axis of FIG. 7 is the crankangle CA, and the execution timing of each injection is indicated.

In step S48, a basic main injection amount Toutmbs(k) is calculated bythe following equation (14). The calculated basic main injection amountToutmbs(k) and the dividing ratio Rm1 are applied to the followingequation (15), to calculate a first main injection basic amount Toutm1bs(k). Further, the first main injection amount Toutm1(k) is calculatedby the equation (16).

Toutmbs(k)=Tout(k)−Toutp(k)  (14)

Toutm1(k)=Rm1×Toutmbs(k)  (15)

In step S49, the second main injection amount Toutm2(k) is calculated bythe following equation (17).

Toutm2(k)=Toutmbs(k)−Toutm1(k)  (17)

In step S50, it is determined whether or not the NOx reduction mode flagFRedMod is equal to “1”. If the answer to step S50 is affirmative (YES),the process proceeds to step S51, in which a Toutpost map shown in FIG.6( c) is retrieved according to the engine rotational speed NE and theengine load target value Pmecmd, to calculate a post injection amount (afuel injection amount of the post injection) Toutpost. The Toutpost mapis set so that the post injection amount Toutpost increases as theengine rotational speed NE increases and/or the engine load target valuePmecmd increases. By increasing the post injection amount Toutpostcorresponding to an increase in the intake air flow rate, theconcentration of reducing components in the exhaust gases is controlledto the desired value.

If FRedMod is equal to “0” in step S50, the process immediately proceedsto step S52.

In step S52, it is determined whether or not the correlation parametera(k) is equal to or less than a determination threshold value AOBDLMT.If the answer to step S52 is affirmative (YES), it is determined thatthe exhaust gas recirculation device has failed (for example, cloggingof the EGR valve 6 has occurred), and a warning lamp is turned on (stepS53). If a(k) is greater than AOBDLMT, the process immediately ends.

FIG. 8 is a flowchart of the state determination process of the EGRamount executed in step S44 of FIG. 4.

In step S31, the estimated exhaust gas recirculation amount EGRhat iscalculated using the self-organizing map (SOM) described above. That is,the neuron NRi is selected according to the EGR valve lift amountcommand value Lcmd and the boost pressure PB, and the weightingcoefficient vector Ci corresponding to the neuron NRi and the inputparameters are applied to the equation (12), to calculate the estimatedexhaust gas recirculation amount EGRhat.

In step S32, the actual exhaust gas recirculation amount EGRact isdetected by the exhaust gas recirculation amount sensor 26. In step S33,the correlation parameters a(k) and b(k), which indicate therelationship between the estimated exhaust gas recirculation amountEGRhat and the actual exhaust gas recirculation amount EGRact, arecalculated with a sequential identifying (sequential statisticalprocessing) algorithm.

FIG. 9 illustrates the relationships between the estimated exhaust gasrecirculation amount EGRhat and the actual exhaust gas recirculationamount EGRact. With respect to the reference engine, the estimatedexhaust gas recirculation amount EGRhat substantially coincides with theactual exhaust gas recirculation amount EGRact. Accordingly, data points(hereinafter referred to as “correlation data points”) defined by theestimated exhaust gas recirculation amount EGRhat and the actual exhaustgas recirculation amount EGRact distribute, as shown in FIG. 9( a) forexample, in the region RD1 with hatching. The region RD1 is definedaround the central straight line L1 and the inclination of the line L1is equal to “1”.

With respect to an engine having a characteristic that the actualexhaust gas recirculation amount EGRact is comparatively great comparedwith the reference engine, the correlation data points are distributed,for example, in the region RD2 of FIG. 9( b). Conversely, with respectto an engine having a characteristic that the actual exhaust gasrecirculation amount EGRact is comparatively small due to, for example,clogging of the EGR valve, the correlation data points distribute in theregion RD3 of FIG. 9B(b), for example. If the state of clogging becomesworse, the distribution of the correlation data points shifts to theregion RD4.

Therefore in this embodiment, the correlation parameters a(k) and b(k)which correspond to the approximated straight line L2 as shown in FIG.9( c), are calculated from the distribution of the correlation datapoints, wherein the engine control parameter is corrected according tothe correlation parameter a(k) indicative of the inclination of theapproximated straight line L2. Further, if the correlation parametera(k) is less than the determination threshold value AOBDLMT (forexample, when the correlation data points distribute in the region RD4),it is determined that the exhaust gas recirculation device has failed,and the warning lamp is turned on (FIG. 4, steps S52 and S53).

The calculation method of the correlation parameters a(k) and b(k) isdescribed below. The sequential identifying algorithm is a least squaresmethod algorithm for calculating the present values a(k) and b(k) of thecorrelation parameters, based on the present values (latest values)EGRhat(k) and EGRact(k) of the data to be processed and the precedingvalues a(k−1) and b(k−1) of the correlation parameters.

If a correlation parameter vector θCR(k) including the correlationparameters a(k) and b(k) as elements is defined by the followingequation (21), the correlation parameter vector θCR(k) is calculated bythe following equation (22) according to the sequential identifyingalgorithm.

θCR(k)^(T) =[a(k)b(k)]  (21)

θCR(k)=θCR(k−1)+KP(k)×eid(k)  (22)

in the equation (22), “eid(k)” is an identification error defined by thefollowing equations (23) and (24). “KP(k)” is a gain coefficient vectordefined by the following equation (25), and “P(k)” in the equation (25)is a second-order square matrix calculated by the following equation(26).

$\begin{matrix}{{{eid}(k)} = {{{EGRact}(k)} - {{{\theta CR}\left( {k - 1} \right)}^{T}{\zeta (k)}}}} & (23) \\{{\zeta^{T}(k)} = \left\lbrack {{{EGRhat}(k)}\mspace{14mu} 1} \right\rbrack} & (24) \\\left\lbrack {{Eq}.\mspace{14mu} 2} \right\rbrack & \; \\{{{KP}(k)} = \frac{{P(k)}{\zeta (k)}}{1 + {{\zeta^{T}(k)}{P(k)}{\zeta (k)}}}} & (25) \\{{P\left( {k + 1} \right)} = {\frac{1}{\lambda_{1}}\left( {I - \frac{\lambda_{2}{P(k)}{\zeta (k)}}{\lambda_{1} + {\lambda_{2}{\zeta^{T}(k)}{P(k)}{\zeta (k)}}}} \right){P(k)}}} & (26)\end{matrix}$

The coefficient λ1 in the equation (26) is set to a value from “0” to“1”, and the coefficient λ2 is set to “1”. “I” is a unit matrix.

According to the sequential identifying algorithm by the equations (21)to (26), the statistic processing operation can be simplified.

If the correlation parameters a(k) and b(k) are used, a statisticallyprocessed exhaust gas recirculation amount EGRIs is given by thefollowing equation (27). The equation (27) is a linear expressioncorresponding to the straight line L2 shown in FIG. 9( c).

EGRIs=a(k)><EGRhat+b(k)  (27)

According to the process of FIG. 8, the correlation parameters a(k) andb(k), which indicate the relationship between the actual exhaust gasrecirculation amount EGRact and the estimated exhaust gas recirculationamount EGRhat, are calculated by the sequential statistical processing.Accordingly, even if the boost pressure PB, the engine rotational speedNE, and the like, which are the input parameters of the neural network,fluctuate at comparatively high frequencies due to noise, the influenceof such fluctuation can be eliminated.

FIG. 10 is a flowchart of the state determination process of the leanNOx catalyst executed in step S45 of FIG. 4.

In step S61, an estimated NOx emission amount NOxhat is calculated usinga self-organizing map (hereinafter referred to as “NOx emission amountSOM”) for calculating the NOx emission amount. The NOx emission amountSOM is set corresponding to the reference engine by the same method asthat for the self-organizing map for calculating the estimated exhaustgas recirculation amount EGRhat (hereinafter referred to as “EGR amountSOM”). The NOx emission amount SOM is used in order to calculate theestimated NOx emission amount NOxhat from an input data vector xNOxshown by the following equation (31). That is, the input parameters ofthe NOx emission amount SOM are the lift amount command value Lcmd ofthe EGR valve, the boost pressure PB, the intake air temperature TI, theexhaust pressure PE, the fuel injection amount Tout, the dividing ratioRm1, the intake air flow rate GA, and the engine rotational speed NE.

xNOx=(Lcmd,PB,TI,PE,Tout,Rm1,GA,NE)  (31)

A weighting coefficient vector CNOxi shown by the following equation(32) is stored corresponding to each neuron NRNOxi of the NOx emissionamount SOM.

CNOxi=(CNOx0i,CNOx1i,CNOx2i,CNOx3i,CNOx4i,CNOx5i,CNOx6i,CNOx7i,CNOx8i)  (32)

Therefore, the neuron NRNOxi is first selected according to the liftamount command value Lcmd and the boost pressure PB. Next, the weightingcoefficient vector CNOXi of the selected neuron NRNOxi is applied to thefollowing equation (33), to calculate the estimated NOx emission amountNOxhat.

$\begin{matrix}{{{NO} \times {hat}} = {{{CNO} \times 1i \times {Lcmd}} + {{CNO} \times 2i \times {PB}} + {{CNO} \times 3i \times {TI}} + {{CNO} \times 4i \times {PE}} + {{CNO} \times 5i \times {Tout}} + {{CNO} \times 6i \times {Rm}\; 1} + {{CNO} \times 7i \times {GA}} + {{CNO} \times 8i \times {NE}} + {{CNO} \times 0i}}} & (33)\end{matrix}$

In step S62, a Krsm map shown in FIG. 11 is retrieved according to theengine rotational speed NE and the engine load target value Pmecmd, tocalculate a modifying coefficient Krsm. The modifying coefficient Krsmis applied to a calculation of an amount MNOx of NOx trapped in the leanNOx catalyst in step S65. The Krsm map is set like the Lcmd map forcalculating the lift amount command value Lcmd. That is, the modifyingcoefficient Krsm is set so as to increase in the medium-load andmedium-speed operating region where the exhaust gas recirculation amountis made to increase, since it is necessary to increase a correctiondegree of the NOx emission amount by the correlation parameter a(k) asthe exhaust gas recirculation amount increases.

In step S63, an estimated reducing component emission amount Redhat iscalculated using a self-organizing map (hereinafter referred to as“reducing component emission amount SOM”) for calculating an amount ofreducing components (HC, CO) discharged from the engine. The estimatedreducing component emission amount Redhat takes a value which isconverted so as to indicate an amount of NOx reduced by the dischargedreducing components.

The reducing component emission amount SOM is set corresponding to thereference engine by the same method of the EGR amount SOM, and is usedfor calculating the estimated reducing component emission amount Redhatfrom an input data vector xRed shown by the following equation (34).That is, the input parameters of the reducing component emission amountSOM are the lift amount command value Lcmd of the EGR valve, the boostpressure PB, the intake air temperature TI, the exhaust pressure PE, thefuel injection amount Tout, the dividing ratio Rm1, the post injectionamount Toutpost, the intake air flow rate GA, and the engine rotationalspeed NE.

xRed=(Lcmd,PB,TI,PE,Tout,Rm1,Toutpost,GA,NE)  (34)

A weighting coefficient vector CRedi shown by the following equation(35) is stored corresponding to each neuron NRRedi of the reducingcomponent emission amount SOM.

Credi=(CRed0i,CRed1i,CRed2i,CRed3i,CRed4i,CRed5i,CRed6i,CRed7i,CRed8i,CRed9i)  (35)

Therefore, the neuron NRRedi is first selected according to the liftamount command value Lcmd and the boost pressure PB. Next, the weightingcoefficient vector CRedi of the selected neuron NRRedi is applied to thefollowing equation (36), to calculate the estimated reducing componentemission amount Redhat.

$\begin{matrix}{{Redhat} = {{{CRed}\; 1i \times {Lcmd}} + {{CRed}\; 2i \times {PB}} + {{CRed}\; 3i \times {TI}} + {{CRed}\; 4i \times {PE}} + {{CRed}\; 5i \times {Tout}} + {{CRed}\; 6i \times {Rm}\; 1} + {{CRed}\; 7i \times {Toutpost}} + {{CRed}\; 8i \times {GA}} + {{CRed}\; 9i \times {NE}} + {{CRed}\; 0i}}} & (36)\end{matrix}$

In step S64, it is determined whether or not the NOx reduction mode flagFRedMod is equal to “1”. If FRedMod is equal to “0” in step S64, themodifying coefficient Krsm, the estimated NOx emission amount NOxhat,and the correlation parameter a(k) are applied to the following equation(37), to calculate the trapped NOx amount MNOx(k) (step S65).

MNOx(k)=MNOx(k−1)+Krsm×NOxhat/a(k)  (37)

In step S66, it is determined whether or not the trapped NOx amountMNOx(k) is equal to or greater than a NOx amount determination thresholdvalue MNOxLMT. If the answer to step S66 is affirmative (YES), the NOxreduction mode flag FRedMod is set to “1” (step S67). If the trapped NOxamount MNOx(k) dose not reach the NOx amount determination thresholdvalue MNOxLMT, the process immediately ends.

If the NOx reduction mode flag FRedMod is set to “1”, the answer to stepS64 becomes affirmative (YES), and the process proceeds to step S68, inwhich the estimated reducing component emission amount Redhat is appliedto the following equation (38), to update the trapped NOx amount MNOx(k)in the decreasing direction.

MNOx(k)=MNOx(k−1)−Redhat  (38)

In step S69, it is determined whether or not the trapped NOx amountMNOx(k) is equal to or less than “0”. If the answer to step S69 isaffirmative (YES), the NOx reduction mode flag FRedMod is set to “0”(step S70). If the trapped NOx amount MNOx(k) is not equal to “0”, theprocess immediately ends.

By applying the correlation parameter a(k) to the calculation of thetrapped NOx amount MNOx(k) (equation (37)), the influence of thecharacteristic variation or aging of the engine can be eliminated,thereby obtaining an accurate value of the trapped NOx amount MNOx.Consequently, the reduction process of the trapped NOx can be performedat an appropriate timing, and the situation such that the NOx emissionamount exceeds the regulation value can be avoided.

In this embodiment, the estimated NOx emission amount NOxhat iscalculated using the NOx emission amount SOM, and the estimated reducingcomponent emission amount Redhat indicative of an amount of NOx reducedwhen performing the NOx reduction process is calculated using thereducing component emission amount SOM. The neural network is able tostore nonlinear relationships among the input parameters. Accordingly,an accurate value of the estimated NOx emission amount NOxhat and theestimated reducing component emission amount Redhat can be obtained notonly in a steady operating condition but also in a transient operatingcondition of the engine, and hence the estimated trapped NOx amount MNOxcan be calculated accurately. Consequently, the NOx reducing process isperformed at an appropriate timing to thereby maintain the NOx emissionamount at a low level.

Further, the estimated NOx emission amount NOxhat is corrected by thecorrelation parameter a(k) and the modifying coefficient Krsm, tocalculate the trapped NOx amount MNOx in the lean NOx catalyst 31.Accordingly, the influence of the characteristic variation and aging ofthe engine (especially the exhaust gas recirculation device) can beeliminated, thereby obtaining an accurate value of the trapped NOxamount MNOx. Consequently, the reduction process of the trapped NOx canbe performed at an appropriate timing, and such a situation that theemission amount of NOx exceeds the regulation value can be avoided.

Further, if the correlation parameter a(k) is equal to or less than thedetermination threshold value AOBDLMT, it is determined that the exhaustgas recirculation device has failed and the warning lamp is turned on.Therefore, the situation where the NOx emission amount exceeds theregulation value can be promptly eliminated.

FIG. 12 shows time charts of an example of the control operation withrespect to the reference engine. The time charts respectively showchanges in the engine rotational speed NE, the boost pressure PB, theactual exhaust gas recirculation amount EGRact, the estimated exhaustgas recirculation amount EGRhat, the trapped NOx amount MNOx calculatedby the above-described equation (37) or (38), an actual trapped NOxamount MNOxact, the NOx reduction mode flag FRedMod, and an amountENOxDS of NOx discharged to the downstream side of the lean NOx catalyst31 (hereinafter referred to as “downstream side NOx emission amount”).In FIG. 12( c), the solid line corresponds to the actual exhaust gasrecirculation amount EGRact, and the dashed line corresponds to theestimated exhaust gas recirculation amount EGRhat. Further, in FIG. 12(d), the solid line corresponds to the actual trapped NOx amount MNOxact,and the dashed line corresponds to the calculated trapped NOx amountMNOx. It is confirmed that the actual trapped NOx amount MNOxact and thecalculated trapped NOx amount MNOx sustantially coincide with eachother. In this state, the NOx reduction process is performed at anappropriate timing, and the downstream side NOx emission amount ENOxDSis maintained at a low level.

FIG. 13 shows an example of the control operation in which a clogging ofthe EGR valve has occurred and the correction of the NOx emission amountby the correlation parameter a(k) is not performed. Changes in theengine rotational speed NE and the boost pressure PB, which are notshown in FIG. 13, are respectively the same as those of FIG. 12. Asshown in FIG. 13( a), the actual exhaust gas recirculation amount EGRactbecomes less than the estimated exhaust gas recirculation amount EGRhatand the start timing of the NOx reduction mode (the timing when FRedModshown in FIG. 13( c) changes from “0” to “1”) delays. Consequently, thedownstream side NOx emission amount ENOxDS shown in FIG. 13( d)increases compared with the example shown in FIG. 12.

FIG. 14 shows an example in which the correction of the emission amountof NOx is performed according to the correlation parameter a(k)(equation (37)) in the example of the control operation shown in FIG.13. The solid line in FIG. 14( b) shows changes in the actual trappedNOx amount MNOxact. The correlation parameter a(k) gradually convergesto a value which reflects the present engine state by repeating thecontrol operation (FIG. 14( a)). The corrected NOx emission amount(=Krsm×NOxhat/a(k)) gradually becomes appropriate with the convergenceof the correlation parameter a(k) and the start timing of the NOxreduction process gradually becomes appropriate as shown in FIG. 14( c).Consequently, the downstream side NOx emission amount ENOxDS graduallydecreases as shown in FIG. 14( d).

In this embodiment, the ECU 20 constitutes the trapped NOx amountestimating means and the reducing process means. Specifically, stepsS61-S68 of FIG. 10 correspond to the trapped NOx amount estimatingmeans, and steps S66, S67, S69, and S70 correspond to the reducingprocess means.

Second Embodiment

FIG. 15 is a schimatic diagram showing a configuration of an internalcombustion engine and a control system therefor according to the presentembodiment. In this embodiment, the exhaust gas recirculation amountsensor 26 is not provided, and an air-fuel ratio sensor 30 is providedin the exhaust pipe 4 immediately downstream of the engine 1. Theair-fuel ratio sensor 30 detects an air-fuel ratio AF of an air-fuelmixture in the combustion chamber by detecting an oxygen concentrationin the exhaust gases, and supplies a detection signal to the ECU 20. Theconfiguration shown in FIG. 15 is the same as that shown in FIG. 1,except for the above-described point.

In this embodiment, a state determination of the exhaust gasrecirculation control and/or the boost pressure control is performedusing the EGR amount SOM, without using the exhaust gas recirculationamount sensor. A distance parameter Discave is calculated as a parameterindicative of the determined control state, and the NOx emission amount,which is applied to calculating the amount of NOx trapped in the leanNOx catalyst, is calculated according to the distance parameter Discave.Further, the failure determination is performed based on the distanceparameter Discave.

Further in this embodiment, a self-organizing map (hereinafter referredto as “first failure EGR amount SOM”) for calculating the estimatedexhaust gas recirculation amount corresponding to a state where afailure has occurred in the exhaust gas recirculation device (forexample, a clogging of the EGR valve), and a self-organizing map(hereinafter referred to as “second failure EGR amount SOM”) forcalculating the estimated exhaust gas recirculation amount correspondingto a state where a failure has occurred in the intake air flow ratesensor 21 (for example, a failure that an error of the detected valueexceeds a predetermined value) are previously set, and the failuredetermination is performed using these SOMs. Specifically, a first and asecond failure distance parameters Diseave and Disaave are calculatedusing the first and the second failure EGR amount SOMs. The failuredeterminations of the exhaust gas recirculation device and the intakeair flow rate sensor 21 are performed according to the first and thesecond failure distance parameters Diseave and Disaave. The presentembodiment is the same as the first embodiment except for the pointsdescribed below.

FIGS. 16 and 17 show a flowchart of the fuel injection control processin this embodiment. In step S111, an Lcmdbs map shown in FIG. 18( a) isretrieved according to the engine rotational speed NE and the engineload target value Pmecmd, to calculate an EGR valve lift amount basiccommand value Lcmdbs. The Lcmdbs map is set similarly to the Lcmd mapshown in FIG. 5( a).

In step S112, the same process as step S42 of FIG. 4 is performed, tocalculate the target boost pressure PBcmd. In step S113, a Kcmd mapshown in FIG. 18( b) is retrieved according to the engine rotationalspeed NE and the engine load target value Pmecmd, to calculate a targetequivalent ratio Kcmd. The target equivalent ratio Kcmd is obtained bycoverting a target air-fuel ratio AFcmd to an equivalent ratio.According to the Kcmd map, the target equivalent ratio Kcmd is basicallyset so as to increase as the engine load target value Pmecmd increases.That is, the target equivalent ratio Kcmd is set to a comparativelysmall value in the low-load engine operating region in order to improvethe fuel consumption. In the medium-speed and medium-load engineoperating region, the target equivalent ratio Kcmd is set to a greatervalue than the value corresponding to a high load operation wherein theengine load target value Pmecmd is equal to the high load value Pmecmd3,since the exhaust gas recirculation amount is set to a comparativelylarge value.

In step S114, the same process as step S43 of FIG. 4 is performed, tocalculate the fuel injection amount Tout. In step S115, the air-fuelratio AF detected by the air-fuel ratio sensor 30 is converted to theequivalent ratio, to calculate a detected equivalent ratio Kact.

In step S116, the EGR valve lift amount command value Lcmd is calculatedwith the sliding mode control so that the detected equivalent ratio Kactcoincides with the target equivalent ratio Kcmd. Specifically, the EGRvalve lift amount command value Lcmd(k) is calculated by adding asliding mode correction term Lsmc(k) to the basic command valueLcmdbs(k) calculated in step S111 by the following equation (41).

Lcmd(k)=Lcmdbs(k)+Lsmc(k)  (41)

The sliding mode correction term Lsmc(k) is calculated by the followingequation (42) as a sum of a reaching law input Lrch(k) and an adaptativelaw input Ladp(k). The reaching law input Lrch(k) and the adaptative lawinput Ladp(k) are respectively calculated by the following equations(43) and (44).

$\begin{matrix}\left\lbrack {{Eq}.\mspace{14mu} 3} \right\rbrack & \; \\{{{Lsmc}(k)} = {{{Lrch}(k)} + {{Ladp}(k)}}} & (42) \\{{{Lrch}(k)} = {{KLrch} \times \sigma \; {L(k)}}} & (43) \\{{{Ladp}(k)} = {{KLadp} \times {\sum\limits_{i = 0}^{k}\; {\sigma \; {L(i)}}}}} & (44)\end{matrix}$

In the equations (43) and (44), “σL” is a switching function valuecalculated by the following equation (45), and “KLrch” and “KLadp” arerespectively a reaching law control gain and an adaptative law controlgain. In the equation (45), “eaf(k)” is a control deviation calculatedby the following equation (46), and “SL” is a switching function settingparameter, which is set to a value between “−1” and “0”.

σL(k)=eaf(k)+SL×eaf(k−1)  (45)

eaf(k)=Kact(k)−Kcmd(k)  (46)

In step S117, the vane opening θvgt(k) of the turbine is calculated withthe sliding mode control so that the detected boost pressure PBcoincides with the target boost pressure PBcmd. Specifically, the vaneopening θvgt(k) is calculated by the following equation (47) as a sum ofa reaching law input θrch(k) and an adaptative law input θadp(k). Thereaching law input θrch(k) and the adaptative law input θadp(k) arerespectively calculated by the following equations (48) and (49).

$\begin{matrix}\left\lbrack {{Eq}.\mspace{14mu} 4} \right\rbrack & \; \\{{\theta \; {{vgt}(k)}} = {{\theta \; {{rcg}(k)}} + {\theta \; {{adp}(k)}}}} & (47) \\{{\theta \; {{rch}(k)}} = {K\; \theta \; {rch} \times \sigma \; {V(k)}}} & (48) \\{{\theta \; {{adp}(k)}} = {K\; \theta \; {adp} \times {\sum\limits_{i = 0}^{k}\; {\sigma \; {V(i)}}}}} & (49)\end{matrix}$

In the equations (48) and (49), “σV” is a switching function valuecalculated by the following equation (50), and “Kθrch” and “Kθadp” arerespectively a reaching law control gain and an adaptative law controlgain. In the equation (50), “epb(k)” is a control deviation calculatedby the following equation (51), and “Sθ” is a switching function settingparameter, which is set to a value between “−1” and “0”.

σV(k)=epb(k)+Sθ×epb(k−1)  (50)

epb(k)=PBcmd(k)−PB(k)  (51)

In step S118, a state determination process of the EGR control and/orthe boost pressure control shown in FIG. 19 is performed. The distanceparameter Discave, the first failure distance parameter Diseave, and thesecond failure distance parameter Disaave, which are described above,are calculated. The distance parameter Discave indicates a degree of adeviation of the present control state from the control state of thereference engine. That is, the distance parameter Discave increases asthe degree of the deviation increases. Specifically, a larger value ofthe distance parameter Discave indicates, for example, that the actualexhaust gas recirculation amount EGRact is less than the flow ratecorresponding to the reference engine due to a clogging of the EGRvalve. Further, the first failure distance parameter Diseave indicatesthat the present control state is closer to the failed statecorresponding to the first failure EGR amount SOM (the state that theexhaust gas recirculation device has failed), as the first failuredistance parameter Diseave decreases. The second failure distanceparameter Disaave indicates that the present control state is closer tothe failed state corresponding to the second failure EGR amount SOM (thestate that the intake air flow rate sensor 21 has failed), as the secondfailure distance parameter Disaave decreases.

In step S119, a state determination process of the lean NOx catalystshown in FIG. 20 is performed. The process of FIG. 20 is obtained bychanging step S65 of FIG. 10 in the first embodiment to step S65 a. Instep S65 a, the distance parameter Discave is applied to the followingequation (71) instead of the equation (37), to calculate the trapped NOxamount MNOx.

MNOx(k)=MNOx(k−1)+(1+Discave(k))×Krsm×Noxhat  (71)

In the equation (71), the second term on the right side which indicatesan emission amount of NOx increases as the distance parameter Discave(k)increases. Accordingly, the trapped NOx amount MNOx corresponding to theincrease in the actual NOx emission amount is obtained. Therefore, bydetermining the start timing of the NOx reduction process using thetrapped NOx amount MNOx calculated by the equation (71) (steps S66 andS67), the NOx reduction process is started at an appropriate timing, tomaintain an amount of NOx discharged to the downstream side of the leanNOx catalyst 31, at a comparatively low level.

Returning to FIG. 16, in step S120, the pilot injection amount Toup iscalculated like step S46 of FIG. 4. In step S121, the dividing raito Rmiis calculated like step S47 of FIG. 4.

In steps S122 and S123, the first main injection amount Toutm1(k) andthe second main injection amount Toutm2 are calculated like steps S48and S49.

In step S124 of FIG. 17, it is determined whether or not the reductionmode flag FRedMod is equal to “1”. If the answer to step S124 isaffirmative (YES), the post injection amount Toutpost is calculated likestep S51 of FIG. 4 in the first embodiment (step S125). That is, in theNOx reduction process, the post injection IPOST is performed and thereducing components are supplied to the exhaust system. After executionof step S125, the process proceeds to step S131. If the answer to stepS124 is negative (NO), the process immdiately proceeds to step S131.

In step S131, it is determined whether or not the distance parameterDiscave(k) is greater than a first failure determination threshold valueDISOBDLMT. If the answer to step S131 is negative (NO), it is determinedthat the engine is normal, and the process immediately ends.

If Discave is greater than DISOBDLMT in step S131, it is determined thatany failure has occurred, and it is further determined whether or notthe first failure distance parameter Diseave(k) is greater than a secondfailure determination threshold value DISOBDLMTE (step S132). If theanswer to step S132 is negative (NO), it is determined that the exhaustgas recirculation device has failed, and the warning lamp indicative ofthe failure is turned on (step S133).

If Diseave(k) is greater than DISOBDLMTE in step S132, it is furtherdetermined whether or not the second distance parameter Disaave isgreater than a third failure determination threshold value DISOBDLMTA(step S134). If the answer to step S94 is negative (NO), it isdetermined that the intake air flow rate sensor 21 has failed, and thewarning lamp indicative of the failure is turned on (step S135).

If Disaave(k) is greater than DISOBDLMTA in step S134, it is determinedthat any device mounted on the engine 1 other than the exhaust gasrecirculation device and the intake air flow rate sensor 21 has failed,and the warning lamp indicative of the failure is turned on (step S136).

FIG. 19 is a flowchart of a process for performing the statedetermination of the EGR control and/or the boost pressure control. Thisprocess is performed in step S118 of FIG. 16.

In step S101, the EGR valve lift amount command value Lcmd(k) and thedetected boost pressure PB(k) are applied to the following equation(61), to calculate a two-dimensional distance Disci(k) with respect toall neurons of the EGR amount SOM. In the equation (61), “Lcmdci” and“PBci” are coordinates of the i-th neuron NRi (i=1 to M) on thetwo-dimensional plane shown in FIG. 2.

[Eq. 5]

Disci(k)=√{square root over ((Lcmdci−Lcmd(k))²+(PBci−PB(k))²)}{squareroot over ((Lcmdci−Lcmd(k))²+(PBci−PB(k))²)}  (61)

In step S102, a minimum value Discmin(k) of the two-dimensionaldistances Disci(k) which are calculated with respect to the “M” neurons,is calculated by the following equation (62).

Discmin(k)=min(Disc1(k), Disc2(k), . . . , DiscM(k))  (62)

In step S103, a moving averaging calculation of the minimum valueDiscmin(k) (the number of data: Nave+1) is performed by the followingequation (63), to calculate the distance parameter Discave(k).

$\begin{matrix}\left\lbrack {{Eq}.\mspace{14mu} 6} \right\rbrack & \; \\{{{Discave}(k)} = {\frac{1}{{Nave} + 1}{\sum\limits_{j = 0}^{Nave}\; {{Disc}\; {\min \left( {k - j} \right)}}}}} & (63)\end{matrix}$

The distance parameter Discave incicates a deviation amount DEVREF ofthe present state of the EGR control and/or the boost pressure controlfrom the control state with respect to the reference engine. That is,the distance parameter Discave takes a value substantially equal to “0”if the present control state is the same as the control state withrespect to the reference engine. Therefore, the distance parameterDiscave increases as the deviation amount DEVREF increases due to thecharacteristic variation or aging of the engine. Consequently, if thedistance parameter Discave is greater than the first failuredetermination threshold value DISOBDLMT, it is possible to determinethat any abnormality exists in the prsent control state, i.e., any oneof the devices mounted on the engine 1 has failed (FIG. 17, step S131).

In step S104, a calculation similar to that of step S101 is performedusing the first failure EGR amount SOM described above, and atwo-dimensional distance Disei(k) is calculated by the followingequation (64) with respect to all neurons of the first failure EGRamount SOM. In the equation (64), “Lcmdei” and “PBei” are coordinates ofthe i-th neuron (i=1 to M) of the first failure EGR amount SOM, on thetwo-dimensional plane defined by Lcmd and PB.

[Eq. 7]

Disei(k)=√{square root over ((Lcmci−Lcmcd(k))²+(PBei−PB(k))²)}{squareroot over ((Lcmci−Lcmcd(k))²+(PBei−PB(k))²)}  (64)

In step S105, a minimum value Disemin(k) of the calculatedtwo-dimensional distances Disei(k) with respect to the “M” neurons, iscalculated by the following equation (65).

Disemin(k)=min(Dise1(k), Dise2(k), . . . , DiseM(k))  (65)

In step S106, a moving averaging calculation of the minimum valueDisemin(k) is performed by the following equation (66), to calculate thefirst failure distance parameter Diseave(k).

$\begin{matrix}{{{Diseave}(k)} = {\frac{1}{{Nave} + 1}{\sum\limits_{j = 0}^{Nave}\; {{Dise}\; {\min \left( {k - j} \right)}}}}} & (66)\end{matrix}$

The first failure distance parameter Diseave indicates a deviationamount DEVFL1 of the present state of the EGR control and/or the boostpressure control from the control state corresponding to the firstfailure EGR amount SOM. That is, if the present control state is thesame as the control state corresponding to the first failure EGR amountSOM, the first failure distance parameter Diseave is substantially equalto “0”. The first failure distance parameter Diseave increases as thedeviation amount DEVFL1 increases. Consequently, if the first failuredistance parameter Diseave is equal to or less than the second failuredetermination threshold value DISOBDLMTE, it is possible to determinethat the exhaust gas recirculation device has failed (FIG. 17, stepsS132, S133).

In step S107, a calculation similar to that of step S101 is performedusing the second failure EGR amount SOM described above, and atwo-dimensional distance Disai(k) is calculated by the followingequation (67) with respect to all neurons of the second failure EGRamount SOM. In the following equation (67), “Lcmdai” and “PBai” in theequation (67) are coordinates of the i-th neuron (i=1 to M) of thesecond failure EGR amount SOM, on the two-dimensional plane defined byLcmd and PB.

[Eq. 9]

Disai(k)=√{square root over ((Lcmdai−Lcmd(k))²+(PBai−PB(k))²)}{squareroot over ((Lcmdai−Lcmd(k))²+(PBai−PB(k))²)}  (67)

In step S108, a minimum value Disamin(k) of the two-dimensionaldistances Disai(k) with respect to the “M” neurons, is calculated by thefollowing equation (68).

Disamin(k)=min(Disa1(k), Disa2(k), . . . , DisaM(k))  (68)

In step S109, a moving averaging operation of the minimum valueDisamin(k) is performed by the following equation (69), to calculate thesecond failure distance parameter Disaave(k).

$\begin{matrix}\left\lbrack {{Eq}.\mspace{14mu} 10} \right\rbrack & \; \\{{{Disaave}(k)} = {\frac{1}{{Nave} + 1}{\sum\limits_{j = 0}^{Nave}\; {{Disa}\; {\min \left( {k - j} \right)}}}}} & (69)\end{matrix}$

The second failure distance parameter Disaave indicates a deviationamount DEVFL2 of the present state of the EGR control and/or the boostpressure control from the control state corresponding to the secondfailure EGR amount SOM. That is, if the present control state is thesame as the control state corresponding to the second failure EGR amountSOM, the second failure distance parameter Disaave is substantiallyequal to “0”. The second failure distance parameter Diseave increases asthe deviation amount DEVFL2 increases. Consequently, if the secondfailure distance parameter Diseave is equal to or less than the thirdfailure determination threshold value DISOBDLMTA, it is possible todetermine that the intake air flow rate sensor 21 has failed (FIG. 17,steps S134, S135).

As described above, in this embodiment, the failure determination isperformed by comparing the distance parameter Discave, the failuredistance parameters Diseave and Disaave with the failure determinationthreshold values. Therefore, it is possible to determine a failure ofthe devices mounted on the engine 1 without any special sensor fordetermining the failure.

By previously setting the first failure EGR amount SOM corresponding tothe state in which the exhaust gas recirculation device has failed, andthe second failure EGR amount SOM corresponding to the state in whichthe intake air flow rate sensor 21 has failed, and performing thefailure determination by the first and second failure distanceparameters Diseave and Disaave calculated using these failure SOMs, afailure of the exhaust gas recirculation device or the intake air flowrate sensor can be determined.

FIG. 21 shows time charts of an example of the control operation withrespect to the reference engine. These time charts respectively showchanges in the engine rotational speed NE, the target boost pressurePBcmd, the boost pressure PB, the EGR valve lift amount basic commandvalue Lcmdbs, the EGR valve lift amount command value Lcmd, the trappedNOx amount MNOx calculated by the equation (71) or (38), the actualtrapped NOx amount MNOxact, the NOx reduction mode flag FRedMod, and thedownstream side NOx emission amount ENOxDS. In FIG. 21( b), the solidline corresponds to the detected boost pressure PB, and the dashed linecorresponds to the target boost pressure PBcmd. The two linessubstantially coincide with each other. In FIG. 21( c), the solid linecorresponds to the lift amount command value Lcmd, and the dashed linecorresponds to the lift amount basic command value Lcmdbs. The two linessubstantially coincide with each other. Further, in FIG. 21( d), thesolid line corresponds to the actual trapped NOx amount MNOxact, and thedashed line corresponds to the calculated trapped NOx amount MNOx. Theactual trapped NOx amount MNOxact and the calculated trapped NOx amountMNOx substantially coincide with each other. In this state, the NOxreduction process is performed at an appropriate timing, therebymaintaining the downstream NOx emission amount ENOxDS at a comparativelylow level.

FIG. 22 shows an example of the control operation in which a clogging ofthe EGR valve has occurred and the correction of the emission amount ofNOx by the distance parameter Discave is not performed. Changes in theengine rotational speed NE, which are not shown in FIG. 22, is the sameas that of FIG. 21. As shown in FIG. 22( a), a difference between thetarget boost pressure PBcmd and the detected boost pressure PB graduallyincreases, and a difference between the lift amount basic command valueLcmdbs and the lift amount command value Lcmd also gradually increasesas shown in FIG. 22( b). This is because the two feedback controls(steps S116 and S117) are operating to compensate the influence ofreduction in the actual exhaust gas recirculation amount EGRact.Therefore, as shown in FIG. 22( c), the actual trapped NOx amountMNOxact becomes greater than the calculated trapped NOx amount MNOxshown by the dashed line. Consequently, as shown in FIG. 22( d), thestart timing of the NOx reduction process (timing at which FRedModchanges from “0” to “1”) delays, and the downstream NOx emission amountENOxDS increases immediately before the start of the NOx reductionprocess (FIG. 22( e)).

FIG. 23 shows an example of the control operation in which the NOxemission amount is corrected according to the distance parameterDiscave(k) (equation (71)) in the example shown in FIG. 22. The solidline of FIG. 23( b) shows changes in the actual trapped NOx amountMNOxact. The distance parameter Discave(k) gradually converges to avalue which reflects the present engine state by repeating the controloperation (FIG. 23( a)). The corrected NOx emission amount(=(1+Discave(k))×Krsm×NOxhat) becomes appropriate along with theconvergence of the distance parameter Discave(k), and the start timingof the NOx reduction process gradually becomes appropriate as shown inFIG. 23( c). Consequently, the downstream side NOx emission amountENOxDS gradually decreases as shown in FIG. 23( d).

As described above, in the present embodiment, the estimated NOxemission amount NOxhat is calculated using the NOx emission amount SOM,and the estimated NOx emission amount Noxhat is corrected by thedistance parameter Discave(k) and the modifying coefficient Krsm, tocalculate the trapped NOx amount MNOx of the lean NOx catalyst 31.Accordinglly, the influence of the characteristic variation or aging ofthe engine (especially the exhaust gas recirculation device) can beeliminated, thereby obtaining an accurate value of the trapped NOxamount MNOx. Consequently, it is possible to perform the reductionprocess of the trapped NOx at an appropriate timing, and such asituation that the emission amount of NOx exceeds the regulation valuecan be avoided.

In this embodiment, Steps S61 to S65 a and S68 of FIG. 20 correspond tothe trapped NOx amount estimating means, and steps S66, S67, S69, andS70 correspond to the reducing process means.

The present invention is not limited to the embodiments described above,and various modifications may be made. For example, in the firstembodiment, not only the self-organizing map but also what is known asthe so-called perceptron, as shown in FIGS. 24 and 25, are applicable asthe neural network.

The neural network shown in FIG. 24 is configured for calculating theestimated exhaust gas recirculation amount EGRhat. This neural networkhas a three-layer structure of an input layer, an intermediate layer,and an output layer, and further includes delay blocks 101 to 107 fordelaying the input data by one sampling period. The well-knownback-propagation learning algorithm is adopted as a learning algorithm.It is to be noted that other methods, such as the random search method,may be adopted as the learning algorithm.

The parameters which are input to the neurons of the input layer are theinput parameters of the EGR amount SOM described above, i.e., the liftamount command value Lcmd, the boost pressure PB, the exhaust pressurePE, the intake air temperature TI, the fuel injection amount Tout, theintake air flow rate GA, the engine rotational speed NE, and thepreceding values of these parameters (except for the intake airtemperature TI) detected one sampling period before, and the precedingvalue of the estimated exhaust gas recirculation amount EGRhat which isthe output parameter of the neural network calculated one samplingperiod before.

If the input parameter is expressed by Ui (i=1 to Ni), each inputparameter Ui is weighted by a coupling coefficient matrix, and theweighted input parameters (Wij·Ui) are input to each neuron of theintermediate layer. An output Xj(k) (j=1 to Nj) of the intermediatelayer is given by the following equation (81). In the equation (81),“Wij” is a coupling coefficient; “W0” is an additional term; and “F” isan input/output function to which, for example, a sigmoid function or aradial basis function and the like are applied.

$\begin{matrix}\left\lbrack {{Eq}.\mspace{14mu} 11} \right\rbrack & \; \\{{{Xj}(k)} = {F\left( {{\sum\limits_{i = 0}^{Ni}\; {{Wij} \cdot {{Ui}(k)}}} + {W\; 0}} \right)}} & (81)\end{matrix}$

In the output layer, if an output parameter is expressed by Y(k), theoutput parameter Y(k) is given by the following equation (82). In thisequation, “Rj” is a coupling coefficient and “R0” is an additional term.

$\begin{matrix}\left\lbrack {{Eq}.\mspace{14mu} 12} \right\rbrack & \; \\{{Y(k)} = {{\sum\limits_{j = 0}^{Nj}\; {{Rj} \cdot {{Xj}(k)}}} + {R\; 0}}} & (82)\end{matrix}$

The coupling coefficients Wij and Rj, and the additional terms W0 and R0in the equations (81) and (82) are previouly calculated with apredetermined learning algorithm using input data for the learning, andstored in the memory. The engine on which the setting of the neuralnetwork is based is the reference engine of the average characteristiclike the EGR amount SOM described above.

An estimation accuracy in the transient state can be improved byadopting the preceding values of the engine operating parameters and thepreceding value of the output parameter, as the input parameters. In theconfiguration shown in FIG. 24, the data detected one sampling periodbefore are used as the input parameters. Alternatively, the datadetected two sampling periods before, and/or the data detected threesampling periods before may further be used.

The neural network shown in FIG. 25 is configured for calculating theestimated NOx emission amount NOxhat, and has the same structure as thatof the neural network shown in FIG. 24. It is to be noted that thedividing ratio Rm1 of the main injection is added as an input parameter.The estimated NOx emission amount NOxhat can be calculated using theequations similar to the equations (81) and (82).

The neural network for calculating the estimated reducing componentemission amount Redhat can be similarly configured by adding the postinjection amount Toutpost as an input parameter to the neural networkshown in FIG. 25.

In the above described embodient, both of the estimated NOx emissionamount NOxhat and the estimated reducing component emission amountRedhat are calculated using a neural network. Alternatively, any one ofNOxhat and Redhat may calculated using a neural network.

INDUSTRIAL APPLICABILITY

The present invention can be applied to an exhaust gas purifyingapparatus for an gasoline internal combustion engine in addition to thediesel internal combustion engine described above. The present inventioncan be applied also to an exhaust gas purifying apparatus for awatercraft propulsion engine, such as an outboard engine having avertically extending crankshaft.

1. An exhaust gas purifying apparatus for an internal combustion enginehaving a lean NOx catalyst in an exhaust system, said lean NOx catalysttrapping NOx in exhaust gases when the exhaust gases are in an oxidizingstate, and discharging the trapped NOx when the exhaust gases are in anreducing state, said exhaust gas purifying apparatus comprising: trappedNOx amount estimating means for calculating an estimated trapped NOxamount which is an estimated value of an amount of NOx trapped in thelean NOx catalyst using a neural network to which engine operatingparameters indicative of an operating condition of said engine areinput, wherein said neural network outputs at least one controlparameter which is relevant to said lean NOx catalyst; and reducingprocess means for performing a reducing process of the NOx trapped insaid lean NOx catalyst, according to the estimated trapped NOx amount.2. The exhaust gas purifying apparatus according to claim 1, whereinsaid neural network is a neural network to which a self-organizing mapalgorithm is applied.
 3. The exhaust gas purifying apparatus accordingto claim 1, wherein the at least one control parameter relevant to saidlean NOx catalyst is an estimated value of an amount of NOx dischargedfrom said engine or an estimated value of an amount of reducingcomponents discharged from said engine.
 4. The exhaust gas purifyingapparatus according to claim 2, wherein the at least one controlparameter relevant to said lean NOx catalyst is an estimated value of anamount of NOx discharged from said engine or an estimated value of anamount of reducing components discharged from said engine.
 5. An exhaustgas purifying method for an internal combustion engine having a lean NOxcatalyst in an exhaust system, said lean NOx catalyst trapping NOx inexhaust gases when the exhaust gases are in an oxidizing state, anddischarging the trapped NOx when the exhaust gases are in an reducingstate, said exhaust gas purifying method comprising the steps of: a)calculating an estimated trapped NOx amount which is an estimated valueof an amount of NOx trapped in the lean NOx catalyst using a neuralnetwork to which engine operating parameters indicative of an operatingcondition of said engine are input, wherein said neural network outputsat least one control parameter which is relevant to said lean NOxcatalyst; and b) performing a reducing process of the NOx trapped insaid lean NOx catalyst, according to the estimated trapped NOx amount.6. The exhaust gas purifying method according to claim 5, wherein saidneural network is a neural network to which a self-organizing mapalgorithm is applied.
 7. The exhaust gas purifying method according toclaim 5, wherein the at least one control parameter relevant to saidlean NOx catalyst is an estimated value of an amount of NOx dischargedfrom said engine or an estimated value of an amount of reducingcomponents discharged from said engine.
 8. The exhaust gas purifyingmethod according to claim 6, wherein the at least one control parameterrelevant to said lean NOx catalyst is an estimated value of an amount ofNOx discharged from said engine or an estimated value of an amount ofreducing components discharged from said engine.